DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification

Mehwish Zafar, Javeria Amin (), Muhammad Sharif, Muhammad Almas Anjum, Ghulam Ali Mallah and Seifedine Kadry
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Mehwish Zafar: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
Javeria Amin: Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan
Muhammad Sharif: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
Muhammad Almas Anjum: National University of Technology (NUTECH), Islamabad 44000, Pakistan
Ghulam Ali Mallah: Department of Computer Science, Shah Abdul Latif University, Khairpur 66111, Pakistan
Seifedine Kadry: Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway

Mathematics, 2023, vol. 11, issue 2, 1-18

Abstract: The development of abnormal cell growth is caused by different pathological alterations and some genetic disorders. This alteration in skin cells is very dangerous and life-threatening, and its timely identification is very essential for better treatment and safe cure. Therefore, in the present article, an approach is proposed for skin lesions’ segmentation and classification. So, in the proposed segmentation framework, pre-trained Mobilenetv2 is utilised in the act of the back pillar of the DeepLabv3+ model and trained on the optimum parameters that provide significant improvement for infected skin lesions’ segmentation. The multi-classification of the skin lesions is carried out through feature extraction from pre-trained DesneNet201 with N × 1000 dimension, out of which informative features are picked from the Slim Mould Algorithm (SMA) and input to SVM and KNN classifiers. The proposed method provided a mean ROC of 0.95 ± 0.03 on MED-Node, 0.97 ± 0.04 on PH2, 0.98 ± 0.02 on HAM-10000, and 0.97 ± 0.00 on ISIC-2019 datasets.

Keywords: skin cancer; skin segmentation; skin classification; melanoma; DeepLabv3+; CNN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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